.png){kind=icon}
返回
Variational quantum tensor networks classifiers
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2021.04.074.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Tensor networks (TN) are a method of decomposing high-rank tensors into tractable lower rank. In this paper, we present a classification algorithm for variational quantum tensor networks (VQTN), which has higher performance on near-term processors. Motivated by the hybrid quantum-classical architecture, the truncated quantum tensor networks (QTN) outputs are fed into a classical neural network. We then utilize kernel encoding, circuit models, multiple readouts, and stochastic gradient descent to achieve shallow quantum circuits. Finally, we deploy the QTN and VQTN algorithms on the TensorFlow Quantum processor by using the Iris and MNIST data sets. Our algorithm experimentally costs only half of the qubits with an average accuracy of 93.72%. Compared with the QTN algorithm, the accuracy is improved by 7.71%. (c) 2021 Elsevier B.V. All rights reserved.
Keyword:
Tensor networks
Quantum computing
Quantum machine learning
Quantum algorithm
Quantum circuit
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
分享收藏Towards a cyanobacterial biorefinery: Carbohydrate fingerprint, biocomposition and enzymatic hydrolysis of Nostoc biomass走向蓝细菌生物精炼厂: Nostoc生物质的碳水化合物指纹，生物组成和酶促水解
PREAI
分享收藏Stochastic gradient descent for hybrid quantum-classical optimization混合量子-经典优化的随机梯度下降.png)
QUANTUM
OAAI
QUANTUMIF5.4
OAAI分享收藏分享收藏分享收藏
查看更多内容